code: def __init__(
      self,
      batch_size,
      num_threads,
      device_id,
      num_gpus,
      data_paths,
      prefetch,
      fp16,
      nhwc,
  ):
      super(RN50Pipeline, self).__init__(
          batch_size,
          num_threads,
          device_id,
          prefetch_queue_depth=prefetch,
      )
      self.input = ops.readers.File(
          file_root=data_paths[0],
          shard_id=device_id,
          num_shards=num_gpus,
      )
      self.decode_gpu = ops.decoders.Image(
          device="mixed", output_type=types.RGB
      )
      self.res = ops.RandomResizedCrop(device="gpu", size=(224, 224))
  
      layout = types.args.nhwc if nhwc else types.NCHW
      out_type = types.FLOAT16 if fp16 else types.FLOAT
  
      self.cmnp = ops.CropMirrorNormalize(
          device="gpu",
          dtype=out_type,
          output_layout=layout,
          crop=(224, 224),
          mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
          std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
      )
      self.coin = ops.random.CoinFlip(probability=0.5)
  
file_path: DALI/dali/test/python/test_RN50_data_fw_iterators.py
imports: from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.types
  import argparse
  import time
label_desc: Use image node_id 1 to create a new RN50Pipeline object from inherited base classes: Pipeline with example: obj = RN50Pipeline(batch_size, num_threads, device_id, num_gpus, data_paths, prefetch, fp16, nhwc)